Why a prototype isn't automatically production-ready
A prototype is deliberately optimized to make an idea tangible quickly – not to verify every dependency or check every line of code for security flaws. But those deliberately skipped steps aren't a minor detail once real users and real data are involved: they map directly onto the risks from the previous three modules of this course.
The four questions before go-live
First: was a dependency audit performed, verifying every AI-suggested package name against the official registry – as protection against slopsquatting? Second: was the code specifically checked for the six most common vulnerability classes (hardcoded credentials, missing authorization, SSRF, unvalidated input, missing security headers, hallucinated dependencies)? Third: was the review threshold described in the governance module actually applied, instead of letting the high AI share slide at prototype speed? Fourth: was it checked which data flowed to the AI coding tool during development – and whether real production data was among it?
The most common mistake: simply keeping prototype speed
The most common mistake in this transition isn't technical, it's organizational: the speed that made the prototype fast gets kept unreflectively, while the four checks above get silently skipped – usually under pressure to get a working demo live quickly.
Practice section: a checklist instead of a gut feeling
The practical consequence: a short, mandatory checklist with exactly these four points that must be ticked off before every prototype-to-production transition – no matter how convincing the demo already looks. That costs a fraction of the time the original prototype saved, but prevents exactly the risks described in detail in the previous three modules.